The word “circadian” is derived from the Latin words circa, meaning about, and dies, or day and refers to processes with 24 hour rhythms. Circadian physiological rhythms are present in organisms across the animal and plant kingdoms. Circadian rhythms are thought to be driven by an internal pacemaker which maintains a self-regulating oscillation with a 24 hour period. Recent research has revealed the molecular structures which form the core of the human circadian pacemaker. The pacemaker serves as a central timing mechanism which synchronizes the rhythms of a wide array of physiological systems.
Circadian Pacemaker Mechanism
Daily fluctuations in human physiology, such as sleeping and body temperature changes, have long been observed; however, it was not until the 1970s that strong experimental evidence of the existence in humans of an endogenous circadian pacemaker emerged. Subsequently, in the 1990s the molecular basis of a central human circadian pacemaker was identified. More specifically, research indicates that a molecular clock located in the suprachiasmatic nucleus (SCN) in the hypothalamus region of the brain maintains an approximate 24 hour rhythm. While evidence of additional peripheral oscillators exists, such as in the liver, the SCN pacemaker is believed to play the central role in regulating circadian timing signals for other physiological systems.
Although the circadian pacemaker has an intrinsic period close to 24 hours, precise synchronization to the external environment is maintained by external stimuli referred to as “zeitgebers” (from German zeit (time) and geber (giver)). For most organisms, including humans, the strongest known zeitgeber is light. The daily transitions between light and dark caused by the earth's rotation relative to the sun create a strong environmental stimulus to which organisms naturally synchronize.
Synchronization of the circadian pacemaker to light occurs through photoreceptors in the retina which have a neural pathway to the SCN that is distinct from the neural pathway of the visual system. Signals arriving to the SCN modify both the phase and amplitude of the pacemaker's oscillations. The duration, intensity, timing of light exposure relative to circadian phase and the pattern of light exposure are all factors which have been observed to influence an organism's circadian pacemaker.
Studying the effects of circadian rhythms is generally not as straight forward as considering 24 hour physiological oscillations. Daily patterns of physical activity and sleep-wake also generally occur on a 24 hour schedule, so it is desirable to distinguish between behavior-induced rhythms (e.g. body temperature rising during the day because of walking) and endogenously driven rhythms (e.g. body temperature rising based on internal circadian thermoregulatory signals).
Two predominant experimental techniques for isolating circadian effects are referred to as the “forced desynchrony” and “constant routine” protocols. Both occur in time isolation laboratories. The forced desynchrony technique forces an individual's sleep and wake schedules to desynchronize from their internal circadian pacemaker. The constant routine technique eliminates sleep/wake effects by keeping individuals awake in a constant environment for more than 24 hours. Based on studies conducted with these protocols, a number of relationships between the circadian pacemaker and various physiological systems has been identified. Non-limiting examples of physiological systems that are, or may be, effected by, or otherwise related to, the circadian pacemaker include: core body temperature (CBT), hormonal melatonin concentration, hormonal cortisol concentration, rate of cell proliferation, the cardiac regulatory system, chemoreceptive respiratory feedback system and cognitive performance (alertness).
Indirect Measurement of Circadian State
Since the human central circadian pacemaker mechanism is inaccessibly located in the brain, its state cannot be measured directly. Some researchers have attempted to indirectly measure a subject's circadian state by inferring the subject's circadian state from measurements of downstream physiological systems. A complication arising out of such indirect inference is that systems with an observable circadian modulation, such as CBT and melatonin secretion for example, are also responsive to other physiological systems and/or environmental stimulus. From the perspective of attempting to infer a subject's circadian state, such physiological systems and/or environmental stimulus are considered to mask the circadian contribution to the observed physiological system. Accordingly, most indirect measurements of a subject's circadian state require methods to “demask” the circadian signal components of an observable system from the other, non-circadian components. Two demasking approaches which have been used in the past involve: physical elimination of time-varying exogenous stimulus; and extraction of exogenous factors using signal processing techniques. Laboratory protocols associated with holding all exogenous stimuli constant may be referred to as “constant routine” techniques. Signal processing methods for extracting exogenous factors may be referred to as “purification” techniques.
The Constant Routine Technique
CBT and hormonal melatonin levels are two physiological systems that tend to exhibit consistent and observable circadian rhythms. However, CBT also responds to physical activity, posture, ambient temperature, and sleep and melatonin secretion is also responsive to ambient light exposure. A constant routine demasking procedure developed by Czeisler (Czeisler, C., J. Allan, S. Strogatz, E. Ronda, R. Sanchez, C. Rios, G. Freitag, G. Richardson, and R. Kronauer, Bright light resets the human circadian pacemaker independent of the timing of the sleep-wake cycle. Science 233:4764, 667-671; 1986 (Czeisler 1986)) attempts to minimize such confounding effects on CBT and melatonin levels, by placing subjects in a strictly controlled laboratory environment. To reduce the effects of sleep-wake transitions and posture changes, the Czeisler technique typically involves: keeping subjects awake for long periods of time (e.g. up to 40 hours) in a semi-recumbent position; setting light exposure to a low level (e.g. to 10 lux); introducing meals at regular intervals (e.g. one hour intervals); and limiting physical activity.
During the constant routine technique, CBT may be measured continuously and the circadian contribution to the CBT (a roughly sinusoidal oscillation with an amplitude of approximately 2° C.) may be monitored. The timing of the minimum of this approximately sinusoidal CBT oscillation typically occurs between 4:00 AM and 5:00 AM and is may be used as an indicator of the circadian state of a subject. The natural circadian melatonin cycle includes an onset in secretion approximately at one's typical sleep time. The timing of this onset is driven by the circadian pacemaker; however, melatonin secretion is also affected by exposure to ambient light. The dim light conditions of the constant routine technique facilitate measurement of the Dim Light Melatonin Onset (DLMO) time.
The constant routine technique is currently accepted as a state of the art method for experimentally assessing the circadian state of a subject and is the primary method by which data have been collected about the circadian-phase-shifting effects of light. Despite the success of the constant routine technique, its application is limited to laboratory environments and often involves subject discomfort (e.g. having to be awake for 40 hours).
The Purification Technique
The “purification” demasking approach is another method of circadian state estimation which attempts to use signal analysis techniques to remove masking contributions from observed physiological phenomena (i.e. to extract the circadian contribution from the observed physiological phenomena). Typically, purification techniques attempt to avoid the restrictive physical constraints of the constant routine technique. Physical activity and sleep represent two well known masking factors associated with the observable phenomena of CBT. Consequently, prior art purification methods have focused on the separation of the effects physical activity and sleep contributions to CBT from the circadian component contribution to CBT. In contrast to the constant routine technique, participants in purification studies have been allowed to follow regular sleep/wake schedules with free ambulatory movement during waking periods.
Waterhouse has developed statistical methods of purification utilizing data from activity sensors. One method involves categorizing activity during waking and sleep periods and then calculating an associated temperature effect from each activity category (Waterhouse, J., D. Weinert, D. Minors, S. Folkard, D. Owens, G. Atkinson, D. Macdonald, N. Sytnik, P. Tucker, and T. Reilly, A comparison of some different methods for purifying core temperature data from humans. Chronobiology International 17:4, 539-566; 2000 (Waterhouse 2000A)). A second method uses a linear regression based on direct mean scores from activity sensors (Waterhouse2000a). Recent developments in purification-based methods have introduced some basic thermoregulatory models (Weinert, D., A. Nevill, R. Weinandy, and J. Waterhouse, The development of new purification methods to assess the circadian rhythm of body temperature in mongolian gerbils. Chronobiology International 20:2, 249-270; 2003).
While results using purification techniques have been shown to be comparable to constant routine techniques in some cases (Waterhouse, J., S. Kao, D. Weinert, B. Edwards, G. Atkinson, and T. Reilly, Measuring phase shifts in humans following a simulated time-zone transition: Agreement between constant routine and purification methods. Chronobiology International 22(5), 829-858; 2005), there remains contention among experts about the accuracy of purification approaches relative to constant routine techniques. A significant limitation of the statistical purification approach is that during periods of significant desynchrony between sleep-wake times and circadian phase, linear methods to separate the two effects from CBT data are inherently unreliable.
Actigraphy
Another approach to indirectly measuring the circadian state of an individual is referred to as actigraphy and is based on the assumption that there is a direct correlation between an individual's rest-activity rhythm and their sleep-wake rhythm and thus their circadian state. Actigraphy involves recording of rest-activity patterns using sensors which record gross physical movement. Typically, actigraphs are implemented using wrist-worn accelerometers.
Actigraphy has been used to indirectly measure the circadian state of cancer patients for timing the delivery of chronomodulated chemotherapy drugs. The type of circadian variation present in actigraph measurements has also been shown to provide an indicator of ‘health status’ of cancer patients. Actigraphy appears attractive for use in field applications, since it is portable and generally non-invasive. However, studies to date have yet to produce strong evidence demonstrating the link between actigraphy and more direct physiological systems known to be correlated to circadian state (e.g. CBT or melatonin). Actigraphy-based techniques have been applied only to individual's following a regular diurnal schedule. As such, confounding factors such as inter-individual variations in circadian phase, differences in behavioral patterns, and irregular schedules, such as arise with shift-work or the like, limit the accuracy and precision of actigraphy-based techniques.
Modeling and Predicting Circadian Dynamics
An alternative to measurement of observable physiological phenomena and using such physiological measurements to estimate an individual's circadian state involve the use of predictive models of circadian pacemaker physiology. Mathematical models describing the dynamic response of the circadian pacemaker have been used to predict the behavior of the circadian pacemaker under specific light exposure scenarios.
Mathematical Models of Circadian State
The most widely accepted model of the circadian pacemaker was developed by Kronauer et al in 1987 based on observations of dose-response relationships between light exposure and circadian phase shifts. Kronauer inferred from experimental data that the model should have both a self-regulating oscillator component representing the internal circadian pacemaker, and a light input response component representing the pathway from light in the eye to a synchronizing input on the oscillator. Subsequent discovery of the molecular functionality of the circadian pacemaker has supported the general physiological basis of the Kronauer model. A refined version of the Kronauer model (the Kronauer-Jewett model) was published in 1999 (Jewett, M., D. Forger, and R. Kronauer, Revised limit cycle oscillator model of human circadian pacemaker. Journal of Biological Rhythms 14:6, 493-499; 1999 (Jewett 1999b)).
FIG. 1 represents a schematic, block-diagram depiction of the Kronauer-Jewett model 10, which comprises a dynamic model including a circadian pacemaker component 12 and a physiological marker component 14 for comparison to a measurable physiological parameter. In the prior art Kronauer-Jewett model 10 of FIG. 1, the measurable physiological parameter is the subject's CBT. Circadian pacemaker component 12 of the Kronauer-Jewett model 10 receives a light input I together with a set of initial conditions xinit, xc init and ninit corresponding to its model variables x, xc and n and uses this information together with its model equations to generate output model variables x, xc. Typical profiles of output model variables x, xc are shown in FIG. 2. It may be observed that output model variables x, xc are approximately sinusoidal in shape with a period of approximately 24 hours and that output model variables x, xc are approximately 90° out of phase with one another.
Physiological marker component 14 of the Kronauer-Jewett model 10 incorporates a minimizer component 16. Minimizer component 16 receives the output model variable x and returns a time at which output model variable x is a minimum xmin. As shown in FIG. 2, the minimum xmin (also referred to as a nadir of the model variable x) occurs once every period of output model variable x or approximately once every 24 hours. The time at which output model variable x is a minimum xmin is referred to FIGS. 1 and 2 as φmin{x}.
The Kronauer-Jewett model 10 also incorporates the experimentally determined observation that the time φmin{x} that the model variable x is a minimum xmin is correlated to the time of the CBT minimum CBTmin. The time that physiological marker component 14 predicts to be the time of CBTmin is referred to in FIG. 1 as φmin{CBT}. As can be seen by observation of summing junction 18, the Kronauer-Jewett model 10 incorporates the experimentally determined relationship that the time φmin{CBT} typically occurs 0.8 hours after the time φmin{x}. Physiological marker component 14 of the Kronauer-Jewett model 10 outputs the time φmin{CBT} of the CBT minimum CBTmin which in turn permits comparison of the Kronauer-Jewett model 10 to measured CBT values. Since the time φmin{x} that the model variable x is a minimum xmin is only output once every approximately 24 hours, it follows that physiological marker component 14 only outputs the time φmin{CBT} of the CBT minimum CBTmin once every approximately 24 hours.
The Kronauer-Jewett circadian pacemaker model 10 has been used with differential-equation-solving algorithms to generate simulations predicting the phase shift of the circadian pacemaker, starting from known initial conditions (xinit, xc init, ninit), in response to a given light exposure pattern (I). This predictive capability has been successfully used to design of experimental protocols and confirm experimental observations of circadian phase shifts in a laboratory context. Despite the apparent usefulness of the Kronauer-Jewett model 10, it has not actually been widely applied in broader contexts—e.g. outside of an experimental laboratory environment.
A number of drawbacks have tended to limit widespread adoption of the prior art Kronauer-Jewett model 10 as a general modeling framework. By way of non-limiting example, such limitations include: (i) the circadian phase of the subject is not presented as a continuous variable which can be monitored (e.g. as an output of model 10) or updated (e.g. as an initial condition of model 10); (ii) the correlation between the circadian phase and physiological marker 14 is not defined in a statistical manner (i.e. Kronauer-Jewett model 10 does not incorporate statistical uncertainties); and (iii) the Kronauer-Jewett model 10 only specifies a correlation to CBT and not to other physiologically observable phenomena.
Alertness Models
One use of circadian physiology models is in the field of human alertness modeling and prediction. Human alertness may also be referred to as human performance. Current models of human alertness incorporate both a sleep-related component and a circadian component; however, most of the widely used human-alertness models assume a fixed circadian phase—e.g. a series of sinusoidal harmonics with a predetermined and constant phase. With such constant phase assumption, scenarios in which the actual circadian phase of a subject may be non-stationary, e.g. shift work or transmeridian travel, cannot be accurately modeled. Some human-alertness models incorporate the potential for changing circadian phase. One such human-alertness model uses a version of the Kronauer model for accommodating variations in the circadian phase (Jewett, M. and R. Kronauer (1999). Interactive mathematical models of subjective alertness and cognitive throughput in humans. Journal of Biological Rhythms 14:6, 588-597; 1999 (Jewett 1999a)). Another such human-alertness model uses a “rule of thumb” for shifting the circadian phase in response to time-zone changes—e.g. a constant rate of change of the circadian phase until the subject is entrained to the new time zone (Akerstedt, T., S. Folkard, and C. Portin, Predictions from the three process model of alertness. AVIATION SPACE AND ENVIRONMENTAL MEDICINE March 75:3, Suppl., A75A83; 2004). The lack of dynamic circadian modeling has been identified as a general need in the context of human alertness prediction.
One of the challenges in applying a human-alertness model incorporating a detailed dynamic circadian pacemaker model to real world scenarios is that current simulation methods require precise specification of initial conditions and complete knowledge of light levels during the course of the simulation. In uncontrolled environments, such as in an actual workplace or in almost any scenario outside of a laboratory, it is difficult to assess both the circadian phase and ambient light levels for a specific individual. It may be this reason that the Kronauer-Jewett model 10 has found application in simulating laboratory environment scenarios, where circadian phase and light levels can be controlled, but has not been widely used in operational scenarios. This inability to apply circadian predictions to real world environments may be partially responsible for the fact that despite a well-established model of the circadian pacemaker, it remains difficult for scientists to provide definitive advice concerning specific circadian adjustment countermeasures.
There is a general desire for systems and methods for predicting a belief in or probability of the circadian state of a subject which may overcome or ameliorate some of the aforementioned issues with the prior art.